Skip to main content

Statistical Software, Siftware and Astronomy

  • Conference paper
Statistical Challenges in Modern Astronomy II

Abstract

This paper discusses statistical, data analytic and related software that is useful in the realm of astronomy and spaces sciences. The paper does not seek to be comprehensive, but rather to present a cross section of software used by practicing statisticians. The general layout is first to discuss commercially available software, then academic research software and finally some possible future directions in the evolution of data-oriented software. We specifically exclude commercial database software from the discussion, although it is relevant. The paper focuses on providing internet (world wide web) pointers for a variety of the software discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Babu, G. J. and Feigelson, E. D. (1996) “Spatial point processes in astronomy,” Journal of Statistical Planning and Inference. 50(3), 311–326.

    Article  MathSciNet  MATH  Google Scholar 

  2. Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language, Wadsworth and Brooks/Cole. Pacific Grove, CA.

    Google Scholar 

  3. Carr, D. and Nicholson, W. (1988) ’ Explor4: A program for exploring four-dimensional data using stereo-ray glyphs, dimensional constraints, rotation and masking,“ In Dynamic Graphics for Statistics. (W. S. Cleveland and M. E. McGill, eds.) Wadsworth Inc., Belmont CA.. 309–329.

    Google Scholar 

  4. Chambers, J. M. and Hastie, T. K.. (eds.) (1992) Statistical Models in S, Wadsworth and Brooks/Cole, Pacific Grove, CA.

    Google Scholar 

  5. Cleveland, W. S. and McGill, M. E. (1988) Dynamic Graphics for Statistics, Wadsworth Inc., Belmont CA.

    Google Scholar 

  6. Feigelson, E. D. and Babu J. G. (eds.) (1993) Statistical Challenges in Modern Astronomy, Springer-Verlag. New York.

    Google Scholar 

  7. Francis, Ivor (1981) Statistical Software: A Comparative Review, North Holland: New York.

    Google Scholar 

  8. Fumas, G. W. and Buja, A. (1994) “Prosection views, dimensional inference through sections and projections” (with discussion). Journal of Computational and Graphical Statistics, 3, 323–385.

    MathSciNet  Google Scholar 

  9. Hayes, Annie (1982) Statistical Software: A Survey and Critique of its Development, Office of Naval Research, Arlington. VA

    Google Scholar 

  10. Huber, Peter J. (1994) “Huge data sets.” COMPSTAT: Proc. in Computat-Statist., 11th Symp., 3–13, (Dutter, R.; Grossmann, W. eds.) PhysicaVerlag, Heidelberg.

    Google Scholar 

  11. Jaschek, C. and Murtagh, F. (eds.) (1990) Errors, Bias and Uncertainties in Astronomy, Cambridge University Press, Cambridge.

    Google Scholar 

  12. Majure, J. J., Cook, D.Cressie, N., Kaiser. M., Lahiri. S. and Symanzik, J. (1995) “Spatial CDF estimation and visualization with application to forest health monitoring,” Computing Science and Statistics, 27, (Rosenberger, J. and Meyer, M., editors).

    Google Scholar 

  13. Murtagh, F. and Heck, A. (eds.) (1988) Astronomy from Large Data-bases-Scientific Objectives and Methodological Approaches, ESO Conference and Workshop Proceedings. No. 28.

    Google Scholar 

  14. Rolfe, E. J. (ed.) (1983) Statistical Methods in Astronomy, European Space Agency Special Publication ESA SP-201.

    Google Scholar 

  15. Statistical Sciences (1993) Statistical Analysis in S-Plus. Version 3.1, Seattle: StatSci, a division of MathSoft, Inc.

    Google Scholar 

  16. Symanzik, J., Majure, J. J. and Cook. D. (1995) “Dynamic graphics in a GIS: A bi-directional link between ArcVlew 2.0 and XGobi, Computing Science and Statistics, 27, (Rosenberger, J. and Meyer. M., editors).

    Google Scholar 

  17. Tierney, Luke (1990) Lisp-Stat, John Wiley and Sons, New York.

    MATH  Google Scholar 

  18. Trumpler, R. J. and Weaver, H. F. (1953) Statistical Astronomy, University of California Press, republished in 1962 by Dover, New York

    MATH  Google Scholar 

  19. Venables, W. N. and Ripley, B. D. (1994) Modern Applied Statistics with S-PLUS, Springer-Verlag, New York.

    MATH  Google Scholar 

  20. Wegman, E. J. (1990) “Hyperdimensional data analysis using parallel coordinates,” J. American Statist. Assoc., 85, 664–675.

    Article  Google Scholar 

  21. Wegman, E. J. (1991) “The grand tour in k-dimensions,” Computing Sci-ence and Statistics: Proceedings of the 22nd Symposium on the Interface, 127–136.

    Google Scholar 

  22. Wegman, E. J. (1995) “Huge data sets and the frontiers of computational feasibility,” Journal of Computational and Graphical Statistics, 4(4), 281–195.

    MathSciNet  Google Scholar 

  23. Wegman, E. J. and Carr, D. B. (1993) “Statistical graphics and visualization,” in Handbook of Statistics 9: Computational Statistics, (Rao, C. R., ed.), 857–958, North Holland, Amsterdam.

    Google Scholar 

  24. Wegman, E. J. and Hayes, A. R. (1988) “Statistical software,” Encyclopedia of Statistical Sciences, 8, 667–674.

    Google Scholar 

  25. Wegman, E. J. and Luo, Qiang (1995) “Visualizing densities,” Technical Report 100, Center for Computational Statistics, George Mason University, Fairfax, VA

    Google Scholar 

  26. Wegman, E. J. and Luo, Qiang (1996) “High dimensional clustering using parallel coordinates and the grand tour,” Technical Report 124, Center for Computational Statistics, George Mason University, Fairfax, VA

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer Science+Business Media New York

About this paper

Cite this paper

Wegman, E.J. et al. (1997). Statistical Software, Siftware and Astronomy. In: Babu, G.J., Feigelson, E.D. (eds) Statistical Challenges in Modern Astronomy II. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1968-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-4612-1968-2_11

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-7360-8

  • Online ISBN: 978-1-4612-1968-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics